Building or learning differentiable simulations of robots and their environments comes with several potential benefits: Massive potential for parallelization [1]}; enabling principled hybrid modeling via analytical models and data-driven neural networks [2]}, [3]} and generally enabling the development of model-based reinforcement learning. The high potential of differentiable simulators has created a lot of interest and progress in developing differentiable physics engines and rigid body simulations that are differentiable [1]}, [2]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}; some of them through analytical gradients [12]}, [13]}, [14]}, [7]}.
